Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations44084
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.4 MiB
Average record size in memory248.0 B

Variable types

Categorical7
Numeric23

Alerts

alt is highly overall correlated with ast and 1 other fieldsHigh correlation
ast is highly overall correlated with altHigh correlation
bmi is highly overall correlated with waist_cm and 1 other fieldsHigh correlation
cholesterol is highly overall correlated with ldl and 1 other fieldsHigh correlation
cholesterol_ratio is highly overall correlated with hdl and 3 other fieldsHigh correlation
eyesight_left is highly overall correlated with eyesight_rightHigh correlation
eyesight_right is highly overall correlated with eyesight_leftHigh correlation
gender is highly overall correlated with height_cm and 3 other fieldsHigh correlation
gtp is highly overall correlated with altHigh correlation
hdl is highly overall correlated with cholesterol_ratio and 1 other fieldsHigh correlation
height_cm is highly overall correlated with gender and 2 other fieldsHigh correlation
hemoglobin is highly overall correlated with gender and 2 other fieldsHigh correlation
hypertension is highly overall correlated with relaxation and 1 other fieldsHigh correlation
ldl is highly overall correlated with cholesterol and 2 other fieldsHigh correlation
ldl_hdl_ratio is highly overall correlated with cholesterol and 3 other fieldsHigh correlation
relaxation is highly overall correlated with hypertension and 1 other fieldsHigh correlation
smoking is highly overall correlated with genderHigh correlation
systolic is highly overall correlated with hypertension and 1 other fieldsHigh correlation
triglyceride is highly overall correlated with cholesterol_ratioHigh correlation
waist_cm is highly overall correlated with bmi and 1 other fieldsHigh correlation
weight_kg is highly overall correlated with bmi and 4 other fieldsHigh correlation
hearing_left is highly imbalanced (83.8%) Imbalance
hearing_right is highly imbalanced (83.5%) Imbalance
hypertension is highly imbalanced (53.8%) Imbalance
ast is highly skewed (γ1 = 23.72604813) Skewed
alt is highly skewed (γ1 = 38.9661536) Skewed

Reproduction

Analysis started2025-04-19 13:13:30.805825
Analysis finished2025-04-19 13:13:58.138791
Duration27.33 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

gender
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
27959 
0
16125 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Length

2025-04-19T09:13:58.175591image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:13:58.222001image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring characters

ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 27959
63.4%
0 16125
36.6%

age
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.178273
Minimum20
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:58.264021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile25
Q140
median40
Q355
95-th percentile65
Maximum85
Range65
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.056772
Coefficient of variation (CV)0.27291179
Kurtosis-0.16514734
Mean44.178273
Median Absolute Deviation (MAD)10
Skewness0.25782831
Sum1947555
Variance145.36574
MonotonicityNot monotonic
2025-04-19T09:13:58.309238image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
40 11964
27.1%
45 5615
12.7%
60 4877
11.1%
50 4381
 
9.9%
55 4023
 
9.1%
35 3555
 
8.1%
30 3180
 
7.2%
25 2791
 
6.3%
20 1289
 
2.9%
65 1061
 
2.4%
Other values (4) 1348
 
3.1%
ValueCountFrequency (%)
20 1289
 
2.9%
25 2791
 
6.3%
30 3180
 
7.2%
35 3555
 
8.1%
40 11964
27.1%
45 5615
12.7%
50 4381
 
9.9%
55 4023
 
9.1%
60 4877
11.1%
65 1061
 
2.4%
ValueCountFrequency (%)
85 13
 
< 0.1%
80 208
 
0.5%
75 481
 
1.1%
70 646
 
1.5%
65 1061
 
2.4%
60 4877
11.1%
55 4023
 
9.1%
50 4381
 
9.9%
45 5615
12.7%
40 11964
27.1%

height_cm
Real number (ℝ)

High correlation 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.64103
Minimum130
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:58.352436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum130
5-th percentile150
Q1160
median165
Q3170
95-th percentile180
Maximum190
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.1898346
Coefficient of variation (CV)0.055817404
Kurtosis-0.6170455
Mean164.64103
Median Absolute Deviation (MAD)5
Skewness-0.14045689
Sum7258035
Variance84.45306
MonotonicityNot monotonic
2025-04-19T09:13:58.394928image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
170 9017
20.5%
165 7844
17.8%
160 7079
16.1%
175 6342
14.4%
155 6035
13.7%
150 3588
 
8.1%
180 2487
 
5.6%
145 965
 
2.2%
185 534
 
1.2%
140 161
 
0.4%
Other values (3) 32
 
0.1%
ValueCountFrequency (%)
130 1
 
< 0.1%
135 3
 
< 0.1%
140 161
 
0.4%
145 965
 
2.2%
150 3588
 
8.1%
155 6035
13.7%
160 7079
16.1%
165 7844
17.8%
170 9017
20.5%
175 6342
14.4%
ValueCountFrequency (%)
190 28
 
0.1%
185 534
 
1.2%
180 2487
 
5.6%
175 6342
14.4%
170 9017
20.5%
165 7844
17.8%
160 7079
16.1%
155 6035
13.7%
150 3588
 
8.1%
145 965
 
2.2%

weight_kg
Real number (ℝ)

High correlation 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.759573
Minimum30
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:58.442791image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45
Q155
median65
Q375
95-th percentile90
Maximum135
Range105
Interquartile range (IQR)20

Descriptive statistics

Standard deviation12.681717
Coefficient of variation (CV)0.19284976
Kurtosis0.21549653
Mean65.759573
Median Absolute Deviation (MAD)10
Skewness0.50221859
Sum2898945
Variance160.82596
MonotonicityNot monotonic
2025-04-19T09:13:58.491997image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
65 6551
14.9%
60 6441
14.6%
70 6152
14.0%
55 5834
13.2%
75 4800
10.9%
50 4401
10.0%
80 3235
7.3%
85 2019
 
4.6%
45 1878
 
4.3%
90 1159
 
2.6%
Other values (12) 1614
 
3.7%
ValueCountFrequency (%)
30 5
 
< 0.1%
35 30
 
0.1%
40 375
 
0.9%
45 1878
 
4.3%
50 4401
10.0%
55 5834
13.2%
60 6441
14.6%
65 6551
14.9%
70 6152
14.0%
75 4800
10.9%
ValueCountFrequency (%)
135 1
 
< 0.1%
130 2
 
< 0.1%
125 6
 
< 0.1%
120 15
 
< 0.1%
115 25
 
0.1%
110 67
 
0.2%
105 146
 
0.3%
100 333
 
0.8%
95 609
1.4%
90 1159
2.6%

waist_cm
Real number (ℝ)

High correlation 

Distinct554
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.962905
Minimum51
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:58.544806image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile67
Q176
median82
Q388
95-th percentile97.3
Maximum129
Range78
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.178336
Coefficient of variation (CV)0.11198159
Kurtosis0.065347716
Mean81.962905
Median Absolute Deviation (MAD)6
Skewness0.21262232
Sum3613252.7
Variance84.241852
MonotonicityNot monotonic
2025-04-19T09:13:58.606189image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 1520
 
3.4%
82 1419
 
3.2%
81 1361
 
3.1%
84 1355
 
3.1%
78 1322
 
3.0%
86 1305
 
3.0%
85 1288
 
2.9%
83 1261
 
2.9%
79 1211
 
2.7%
76 1201
 
2.7%
Other values (544) 30841
70.0%
ValueCountFrequency (%)
51 1
 
< 0.1%
53 1
 
< 0.1%
54 2
 
< 0.1%
55 3
< 0.1%
56 5
< 0.1%
56.2 2
 
< 0.1%
56.4 1
 
< 0.1%
56.6 1
 
< 0.1%
57 7
< 0.1%
57.2 1
 
< 0.1%
ValueCountFrequency (%)
129 1
 
< 0.1%
127.7 1
 
< 0.1%
127 1
 
< 0.1%
124 1
 
< 0.1%
123 1
 
< 0.1%
121 3
< 0.1%
120.9 1
 
< 0.1%
120 2
< 0.1%
119 1
 
< 0.1%
118.5 1
 
< 0.1%

eyesight_left
Real number (ℝ)

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0126758
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:58.661168image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.48691745
Coefficient of variation (CV)0.48082264
Kurtosis181.43453
Mean1.0126758
Median Absolute Deviation (MAD)0.2
Skewness10.008953
Sum44642.8
Variance0.2370886
MonotonicityNot monotonic
2025-04-19T09:13:58.707711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1.2 10084
22.9%
1 9645
21.9%
1.5 6197
14.1%
0.8 4181
9.5%
0.9 4101
9.3%
0.7 3529
 
8.0%
0.6 1961
 
4.4%
0.5 1664
 
3.8%
0.4 964
 
2.2%
0.3 703
 
1.6%
Other values (9) 1055
 
2.4%
ValueCountFrequency (%)
0.1 274
 
0.6%
0.2 367
 
0.8%
0.3 703
 
1.6%
0.4 964
 
2.2%
0.5 1664
 
3.8%
0.6 1961
 
4.4%
0.7 3529
 
8.0%
0.8 4181
9.5%
0.9 4101
9.3%
1 9645
21.9%
ValueCountFrequency (%)
9.9 73
 
0.2%
2 311
 
0.7%
1.9 2
 
< 0.1%
1.8 1
 
< 0.1%
1.6 16
 
< 0.1%
1.5 6197
14.1%
1.3 8
 
< 0.1%
1.2 10084
22.9%
1.1 3
 
< 0.1%
1 9645
21.9%

eyesight_right
Real number (ℝ)

High correlation 

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0085178
Minimum0.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:58.753391image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.8
median1
Q31.2
95-th percentile1.5
Maximum9.9
Range9.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.481965
Coefficient of variation (CV)0.47789438
Kurtosis184.22319
Mean1.0085178
Median Absolute Deviation (MAD)0.2
Skewness10.049422
Sum44459.5
Variance0.23229026
MonotonicityNot monotonic
2025-04-19T09:13:58.800898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1.2 9911
22.5%
1 9909
22.5%
1.5 6045
13.7%
0.8 4286
9.7%
0.9 4170
9.5%
0.7 3437
 
7.8%
0.6 1881
 
4.3%
0.5 1702
 
3.9%
0.4 1025
 
2.3%
0.3 674
 
1.5%
Other values (7) 1044
 
2.4%
ValueCountFrequency (%)
0.1 278
 
0.6%
0.2 388
 
0.9%
0.3 674
 
1.5%
0.4 1025
 
2.3%
0.5 1702
 
3.9%
0.6 1881
 
4.3%
0.7 3437
 
7.8%
0.8 4286
9.7%
0.9 4170
9.5%
1 9909
22.5%
ValueCountFrequency (%)
9.9 71
 
0.2%
2 284
 
0.6%
1.6 15
 
< 0.1%
1.5 6045
13.7%
1.3 6
 
< 0.1%
1.2 9911
22.5%
1.1 2
 
< 0.1%
1 9909
22.5%
0.9 4170
9.5%
0.8 4286
9.7%

hearing_left
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1.0
43034 
2.0
 
1050

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132252
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 43034
97.6%
2.0 1050
 
2.4%

Length

2025-04-19T09:13:58.848087image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:13:58.889186image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 43034
97.6%
2.0 1050
 
2.4%

Most occurring characters

ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43034
32.5%
2 1050
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43034
32.5%
2 1050
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43034
32.5%
2 1050
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43034
32.5%
2 1050
 
0.8%

hearing_right
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.5 MiB
1.0
43013 
2.0
 
1071

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters132252
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 43013
97.6%
2.0 1071
 
2.4%

Length

2025-04-19T09:13:58.931475image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:13:58.972037image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0 43013
97.6%
2.0 1071
 
2.4%

Most occurring characters

ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43013
32.5%
2 1071
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 132252
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43013
32.5%
2 1071
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 132252
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43013
32.5%
2 1071
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 132252
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 44084
33.3%
0 44084
33.3%
1 43013
32.5%
2 1071
 
0.8%

systolic
Real number (ℝ)

High correlation 

Distinct126
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.36519
Minimum71
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.017819image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile100
Q1112
median120
Q3130
95-th percentile143
Maximum240
Range169
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.499198
Coefficient of variation (CV)0.11122792
Kurtosis1.1263834
Mean121.36519
Median Absolute Deviation (MAD)10
Skewness0.41549873
Sum5350263
Variance182.22834
MonotonicityNot monotonic
2025-04-19T09:13:59.076771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 2759
 
6.3%
120 2717
 
6.2%
130 2606
 
5.9%
118 2366
 
5.4%
124 1216
 
2.8%
119 1206
 
2.7%
128 1203
 
2.7%
116 1203
 
2.7%
100 1186
 
2.7%
122 1167
 
2.6%
Other values (116) 26455
60.0%
ValueCountFrequency (%)
71 1
 
< 0.1%
72 1
 
< 0.1%
79 1
 
< 0.1%
80 3
 
< 0.1%
81 6
< 0.1%
82 5
 
< 0.1%
83 5
 
< 0.1%
84 5
 
< 0.1%
85 3
 
< 0.1%
86 14
< 0.1%
ValueCountFrequency (%)
240 1
 
< 0.1%
223 1
 
< 0.1%
220 1
 
< 0.1%
213 1
 
< 0.1%
208 1
 
< 0.1%
204 2
< 0.1%
200 2
< 0.1%
199 3
< 0.1%
198 2
< 0.1%
197 2
< 0.1%

relaxation
Real number (ℝ)

High correlation 

Distinct93
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.934262
Minimum40
Maximum140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.136025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q170
median76
Q382
95-th percentile90
Maximum140
Range100
Interquartile range (IQR)12

Descriptive statistics

Standard deviation9.5771421
Coefficient of variation (CV)0.12612412
Kurtosis0.73859298
Mean75.934262
Median Absolute Deviation (MAD)6
Skewness0.34095322
Sum3347486
Variance91.72165
MonotonicityNot monotonic
2025-04-19T09:13:59.194808image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 4293
 
9.7%
70 4139
 
9.4%
78 2545
 
5.8%
60 1742
 
4.0%
72 1732
 
3.9%
74 1658
 
3.8%
76 1636
 
3.7%
75 1461
 
3.3%
82 1394
 
3.2%
84 1336
 
3.0%
Other values (83) 22148
50.2%
ValueCountFrequency (%)
40 2
 
< 0.1%
42 1
 
< 0.1%
44 3
 
< 0.1%
45 1
 
< 0.1%
46 3
 
< 0.1%
47 3
 
< 0.1%
48 6
 
< 0.1%
49 7
 
< 0.1%
50 27
0.1%
51 44
0.1%
ValueCountFrequency (%)
140 2
< 0.1%
137 1
< 0.1%
136 1
< 0.1%
134 1
< 0.1%
133 1
< 0.1%
132 1
< 0.1%
130 2
< 0.1%
129 1
< 0.1%
128 1
< 0.1%
126 1
< 0.1%

fasting_blood_sugar
Real number (ℝ)

Distinct271
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.993535
Minimum46
Maximum505
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.250716image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile80
Q189
median95
Q3103
95-th percentile128
Maximum505
Range459
Interquartile range (IQR)14

Descriptive statistics

Standard deviation20.240882
Coefficient of variation (CV)0.2044667
Kurtosis39.08854
Mean98.993535
Median Absolute Deviation (MAD)7
Skewness4.6073628
Sum4364031
Variance409.69329
MonotonicityNot monotonic
2025-04-19T09:13:59.307840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 1764
 
4.0%
93 1711
 
3.9%
92 1710
 
3.9%
97 1699
 
3.9%
95 1695
 
3.8%
91 1678
 
3.8%
98 1615
 
3.7%
96 1581
 
3.6%
90 1570
 
3.6%
99 1564
 
3.5%
Other values (261) 27497
62.4%
ValueCountFrequency (%)
46 1
 
< 0.1%
48 1
 
< 0.1%
51 1
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
56 2
 
< 0.1%
57 3
< 0.1%
58 1
 
< 0.1%
59 3
< 0.1%
60 7
< 0.1%
ValueCountFrequency (%)
505 1
< 0.1%
475 1
< 0.1%
423 1
< 0.1%
398 1
< 0.1%
391 1
< 0.1%
386 1
< 0.1%
375 1
< 0.1%
369 2
< 0.1%
365 1
< 0.1%
363 1
< 0.1%

cholesterol
Real number (ℝ)

High correlation 

Distinct282
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean196.80782
Minimum55
Maximum445
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.362617image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum55
5-th percentile141
Q1172
median195
Q3219
95-th percentile259
Maximum445
Range390
Interquartile range (IQR)47

Descriptive statistics

Standard deviation36.110902
Coefficient of variation (CV)0.18348307
Kurtosis0.54881571
Mean196.80782
Median Absolute Deviation (MAD)24
Skewness0.3724775
Sum8676076
Variance1303.9973
MonotonicityNot monotonic
2025-04-19T09:13:59.422269image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199 545
 
1.2%
187 516
 
1.2%
192 515
 
1.2%
197 512
 
1.2%
195 505
 
1.1%
189 504
 
1.1%
198 504
 
1.1%
188 502
 
1.1%
193 502
 
1.1%
180 500
 
1.1%
Other values (272) 38979
88.4%
ValueCountFrequency (%)
55 1
 
< 0.1%
72 1
 
< 0.1%
77 2
< 0.1%
84 1
 
< 0.1%
86 1
 
< 0.1%
87 1
 
< 0.1%
90 2
< 0.1%
91 3
< 0.1%
92 3
< 0.1%
93 3
< 0.1%
ValueCountFrequency (%)
445 1
< 0.1%
442 1
< 0.1%
441 1
< 0.1%
419 1
< 0.1%
410 1
< 0.1%
395 1
< 0.1%
393 1
< 0.1%
386 1
< 0.1%
380 1
< 0.1%
377 1
< 0.1%

triglyceride
Real number (ℝ)

High correlation 

Distinct390
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.77116
Minimum8
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.483144image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile46
Q174
median107
Q3159
95-th percentile274
Maximum999
Range991
Interquartile range (IQR)85

Descriptive statistics

Standard deviation70.655231
Coefficient of variation (CV)0.56177608
Kurtosis2.0593309
Mean125.77116
Median Absolute Deviation (MAD)39
Skewness1.3220634
Sum5544496
Variance4992.1617
MonotonicityNot monotonic
2025-04-19T09:13:59.542110image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71 418
 
0.9%
79 393
 
0.9%
82 393
 
0.9%
83 386
 
0.9%
80 378
 
0.9%
66 374
 
0.8%
69 369
 
0.8%
85 369
 
0.8%
78 368
 
0.8%
67 365
 
0.8%
Other values (380) 40271
91.4%
ValueCountFrequency (%)
8 1
 
< 0.1%
11 1
 
< 0.1%
15 1
 
< 0.1%
16 4
 
< 0.1%
19 2
 
< 0.1%
20 7
< 0.1%
21 6
< 0.1%
22 6
< 0.1%
23 8
< 0.1%
24 12
< 0.1%
ValueCountFrequency (%)
999 1
 
< 0.1%
548 1
 
< 0.1%
466 1
 
< 0.1%
432 1
 
< 0.1%
405 1
 
< 0.1%
399 14
< 0.1%
398 8
< 0.1%
397 14
< 0.1%
396 5
 
< 0.1%
395 8
< 0.1%

hdl
Real number (ℝ)

High correlation 

Distinct125
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.318914
Minimum4
Maximum618
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.596711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile37
Q147
median55
Q366
95-th percentile84
Maximum618
Range614
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.748826
Coefficient of variation (CV)0.25731168
Kurtosis52.199372
Mean57.318914
Median Absolute Deviation (MAD)9
Skewness2.2202778
Sum2526847
Variance217.52788
MonotonicityNot monotonic
2025-04-19T09:13:59.654675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 1325
 
3.0%
50 1324
 
3.0%
54 1309
 
3.0%
56 1309
 
3.0%
53 1295
 
2.9%
55 1281
 
2.9%
49 1265
 
2.9%
52 1263
 
2.9%
47 1261
 
2.9%
48 1257
 
2.9%
Other values (115) 31195
70.8%
ValueCountFrequency (%)
4 2
 
< 0.1%
11 1
 
< 0.1%
14 1
 
< 0.1%
17 1
 
< 0.1%
18 2
 
< 0.1%
21 2
 
< 0.1%
22 2
 
< 0.1%
23 4
 
< 0.1%
24 8
< 0.1%
25 10
< 0.1%
ValueCountFrequency (%)
618 1
 
< 0.1%
359 1
 
< 0.1%
157 1
 
< 0.1%
155 1
 
< 0.1%
148 1
 
< 0.1%
144 1
 
< 0.1%
136 2
< 0.1%
135 2
< 0.1%
133 3
< 0.1%
132 1
 
< 0.1%

ldl
Real number (ℝ)

High correlation 

Distinct283
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.96092
Minimum1
Maximum1860
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.711950image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile63
Q192
median113
Q3136
95-th percentile171
Maximum1860
Range1859
Interquartile range (IQR)44

Descriptive statistics

Standard deviation40.125841
Coefficient of variation (CV)0.34903898
Kurtosis354.74025
Mean114.96092
Median Absolute Deviation (MAD)22
Skewness10.373479
Sum5067937
Variance1610.0831
MonotonicityNot monotonic
2025-04-19T09:13:59.770656image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
110 582
 
1.3%
112 574
 
1.3%
121 559
 
1.3%
111 554
 
1.3%
106 553
 
1.3%
107 550
 
1.2%
116 547
 
1.2%
101 544
 
1.2%
96 541
 
1.2%
114 538
 
1.2%
Other values (273) 38542
87.4%
ValueCountFrequency (%)
1 2
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
11 1
 
< 0.1%
12 4
< 0.1%
13 3
< 0.1%
14 1
 
< 0.1%
15 1
 
< 0.1%
16 5
< 0.1%
ValueCountFrequency (%)
1860 1
< 0.1%
1810 1
< 0.1%
1660 1
< 0.1%
1560 1
< 0.1%
1400 1
< 0.1%
1340 1
< 0.1%
1260 1
< 0.1%
1220 1
< 0.1%
1200 1
< 0.1%
1120 1
< 0.1%

hemoglobin
Real number (ℝ)

High correlation 

Distinct142
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.614207
Minimum4.9
Maximum20.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.827118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4.9
5-th percentile12.1
Q113.6
median14.8
Q315.7
95-th percentile16.8
Maximum20.9
Range16
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.559347
Coefficient of variation (CV)0.10670076
Kurtosis1.2477973
Mean14.614207
Median Absolute Deviation (MAD)1.1
Skewness-0.66575185
Sum644252.7
Variance2.431563
MonotonicityNot monotonic
2025-04-19T09:13:59.885158image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.4 1207
 
2.7%
15 1197
 
2.7%
15.6 1194
 
2.7%
15.3 1191
 
2.7%
15.5 1173
 
2.7%
15.7 1171
 
2.7%
15.2 1118
 
2.5%
14.9 1116
 
2.5%
15.8 1103
 
2.5%
15.1 1102
 
2.5%
Other values (132) 32512
73.8%
ValueCountFrequency (%)
4.9 1
 
< 0.1%
5 2
 
< 0.1%
5.5 2
 
< 0.1%
5.8 2
 
< 0.1%
5.9 1
 
< 0.1%
6.1 1
 
< 0.1%
6.2 1
 
< 0.1%
6.3 5
< 0.1%
6.4 1
 
< 0.1%
6.6 3
< 0.1%
ValueCountFrequency (%)
20.9 1
 
< 0.1%
20.4 1
 
< 0.1%
20 1
 
< 0.1%
19.7 1
 
< 0.1%
19.6 2
 
< 0.1%
19.5 1
 
< 0.1%
19.3 2
 
< 0.1%
19.2 2
 
< 0.1%
19.1 6
< 0.1%
19 4
< 0.1%

urine_protein
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0814581
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:13:59.933621image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum6
Range5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.38887753
Coefficient of variation (CV)0.3595863
Kurtosis39.677181
Mean1.0814581
Median Absolute Deviation (MAD)0
Skewness5.8349304
Sum47675
Variance0.15122573
MonotonicityNot monotonic
2025-04-19T09:13:59.975459image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 41760
94.7%
2 1386
 
3.1%
3 688
 
1.6%
4 180
 
0.4%
5 61
 
0.1%
6 9
 
< 0.1%
ValueCountFrequency (%)
1 41760
94.7%
2 1386
 
3.1%
3 688
 
1.6%
4 180
 
0.4%
5 61
 
0.1%
6 9
 
< 0.1%
ValueCountFrequency (%)
6 9
 
< 0.1%
5 61
 
0.1%
4 180
 
0.4%
3 688
 
1.6%
2 1386
 
3.1%
1 41760
94.7%

serum_creatinine
Real number (ℝ)

Distinct38
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.88552536
Minimum0.1
Maximum11.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.022549image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.6
Q10.8
median0.9
Q31
95-th percentile1.2
Maximum11.6
Range11.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation0.22555141
Coefficient of variation (CV)0.25470914
Kurtosis412.4565
Mean0.88552536
Median Absolute Deviation (MAD)0.1
Skewness10.633209
Sum39037.5
Variance0.050873437
MonotonicityNot monotonic
2025-04-19T09:14:00.076802image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0.9 8963
20.3%
0.8 8319
18.9%
1 7681
17.4%
0.7 5957
13.5%
1.1 4871
11.0%
0.6 3530
 
8.0%
1.2 2312
 
5.2%
0.5 1181
 
2.7%
1.3 702
 
1.6%
1.4 225
 
0.5%
Other values (28) 343
 
0.8%
ValueCountFrequency (%)
0.1 19
 
< 0.1%
0.2 2
 
< 0.1%
0.3 9
 
< 0.1%
0.4 160
 
0.4%
0.5 1181
 
2.7%
0.6 3530
 
8.0%
0.7 5957
13.5%
0.8 8319
18.9%
0.9 8963
20.3%
1 7681
17.4%
ValueCountFrequency (%)
11.6 1
< 0.1%
10.3 1
< 0.1%
10 2
< 0.1%
9.9 1
< 0.1%
7.5 1
< 0.1%
7.4 1
< 0.1%
6.4 1
< 0.1%
5.9 1
< 0.1%
5 1
< 0.1%
3.4 2
< 0.1%

ast
Real number (ℝ)

High correlation  Skewed 

Distinct196
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.79437
Minimum6
Maximum1090
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.135581image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q119
median23
Q328
95-th percentile44
Maximum1090
Range1084
Interquartile range (IQR)9

Descriptive statistics

Standard deviation17.217872
Coefficient of variation (CV)0.66750505
Kurtosis1090.9437
Mean25.79437
Median Absolute Deviation (MAD)4
Skewness23.726048
Sum1137119
Variance296.45512
MonotonicityNot monotonic
2025-04-19T09:14:00.194572image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 3053
 
6.9%
21 2998
 
6.8%
22 2846
 
6.5%
19 2826
 
6.4%
23 2685
 
6.1%
18 2559
 
5.8%
24 2531
 
5.7%
25 2216
 
5.0%
17 2146
 
4.9%
26 1834
 
4.2%
Other values (186) 18390
41.7%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 3
 
< 0.1%
8 3
 
< 0.1%
9 18
 
< 0.1%
10 36
 
0.1%
11 84
 
0.2%
12 225
 
0.5%
13 434
 
1.0%
14 761
1.7%
15 1269
2.9%
ValueCountFrequency (%)
1090 1
< 0.1%
981 1
< 0.1%
976 1
< 0.1%
841 1
< 0.1%
778 1
< 0.1%
656 1
< 0.1%
591 1
< 0.1%
545 1
< 0.1%
527 1
< 0.1%
387 1
< 0.1%

alt
Real number (ℝ)

High correlation  Skewed 

Distinct229
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.469966
Minimum1
Maximum2914
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.250031image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10
Q115
median21
Q330
95-th percentile60
Maximum2914
Range2913
Interquartile range (IQR)15

Descriptive statistics

Standard deviation28.486264
Coefficient of variation (CV)1.076173
Kurtosis3107.6768
Mean26.469966
Median Absolute Deviation (MAD)7
Skewness38.966154
Sum1166902
Variance811.46726
MonotonicityNot monotonic
2025-04-19T09:14:00.306419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2159
 
4.9%
16 2103
 
4.8%
17 2095
 
4.8%
14 2046
 
4.6%
18 2038
 
4.6%
13 1878
 
4.3%
19 1854
 
4.2%
12 1731
 
3.9%
20 1724
 
3.9%
21 1646
 
3.7%
Other values (219) 24810
56.3%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 1
 
< 0.1%
3 4
 
< 0.1%
4 18
 
< 0.1%
5 41
 
0.1%
6 96
 
0.2%
7 212
 
0.5%
8 402
 
0.9%
9 676
1.5%
10 1087
2.5%
ValueCountFrequency (%)
2914 1
< 0.1%
1783 1
< 0.1%
1504 1
< 0.1%
1400 1
< 0.1%
1155 1
< 0.1%
745 1
< 0.1%
740 1
< 0.1%
713 1
< 0.1%
610 1
< 0.1%
577 1
< 0.1%

gtp
Real number (ℝ)

High correlation 

Distinct456
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.736934
Minimum1
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.364906image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q117
median25
Q343
95-th percentile106
Maximum999
Range998
Interquartile range (IQR)26

Descriptive statistics

Standard deviation46.216111
Coefficient of variation (CV)1.1930761
Kurtosis72.304968
Mean38.736934
Median Absolute Deviation (MAD)10
Skewness6.5117151
Sum1707679
Variance2135.929
MonotonicityNot monotonic
2025-04-19T09:14:00.423758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 1707
 
3.9%
15 1689
 
3.8%
14 1651
 
3.7%
17 1638
 
3.7%
18 1586
 
3.6%
13 1481
 
3.4%
19 1452
 
3.3%
20 1403
 
3.2%
21 1340
 
3.0%
22 1288
 
2.9%
Other values (446) 28849
65.4%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 1
 
< 0.1%
3 2
 
< 0.1%
4 1
 
< 0.1%
5 11
 
< 0.1%
6 38
 
0.1%
7 93
 
0.2%
8 188
 
0.4%
9 406
0.9%
10 734
1.7%
ValueCountFrequency (%)
999 1
< 0.1%
961 1
< 0.1%
933 1
< 0.1%
926 1
< 0.1%
910 1
< 0.1%
894 1
< 0.1%
875 1
< 0.1%
873 1
< 0.1%
850 1
< 0.1%
816 1
< 0.1%

dental_caries
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
34804 
1
9280 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 34804
78.9%
1 9280
 
21.1%

Length

2025-04-19T09:14:00.474971image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:14:00.515851image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 34804
78.9%
1 9280
 
21.1%

Most occurring characters

ValueCountFrequency (%)
0 34804
78.9%
1 9280
 
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 34804
78.9%
1 9280
 
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 34804
78.9%
1 9280
 
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 34804
78.9%
1 9280
 
21.1%

tartar
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
1
24485 
0
19599 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 24485
55.5%
0 19599
44.5%

Length

2025-04-19T09:14:00.559547image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:14:00.600384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
1 24485
55.5%
0 19599
44.5%

Most occurring characters

ValueCountFrequency (%)
1 24485
55.5%
0 19599
44.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 24485
55.5%
0 19599
44.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 24485
55.5%
0 19599
44.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 24485
55.5%
0 19599
44.5%

smoking
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
27972 
1
16112 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Length

2025-04-19T09:14:00.644192image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:14:00.684868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring characters

ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 27972
63.5%
1 16112
36.5%

bmi
Real number (ℝ)

High correlation 

Distinct144
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.131814
Minimum14.268728
Maximum42.44898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.731530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum14.268728
5-th percentile19.031142
Q121.604938
median23.875115
Q326.122449
95-th percentile29.411765
Maximum42.44898
Range28.180252
Interquartile range (IQR)4.5175107

Descriptive statistics

Standard deviation3.4369806
Coefficient of variation (CV)0.14242529
Kurtosis0.56531355
Mean24.131814
Median Absolute Deviation (MAD)2.2473342
Skewness0.5476608
Sum1063826.9
Variance11.812836
MonotonicityNot monotonic
2025-04-19T09:14:00.786017image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.22145329 1977
 
4.5%
22.49134948 1739
 
3.9%
23.87511478 1738
 
3.9%
23.4375 1627
 
3.7%
21.484375 1618
 
3.7%
22.03856749 1614
 
3.7%
25.95155709 1589
 
3.6%
22.89281998 1562
 
3.5%
25.71166208 1478
 
3.4%
20.81165453 1463
 
3.3%
Other values (134) 27679
62.8%
ValueCountFrequency (%)
14.26872771 3
 
< 0.1%
14.56815817 2
 
< 0.1%
14.69237833 2
 
< 0.1%
15.30612245 1
 
< 0.1%
15.55555556 13
 
< 0.1%
15.57093426 13
 
< 0.1%
15.625 21
 
< 0.1%
16.32653061 28
0.1%
16.46090535 1
 
< 0.1%
16.52892562 63
0.1%
ValueCountFrequency (%)
42.44897959 1
 
< 0.1%
41.66666667 1
 
< 0.1%
41.62330905 1
 
< 0.1%
41.015625 1
 
< 0.1%
40.81632653 3
< 0.1%
39.5421436 2
 
< 0.1%
39.18367347 7
< 0.1%
39.0625 1
 
< 0.1%
38.58024691 3
< 0.1%
38.56749311 5
< 0.1%

bp_ratio
Real number (ℝ)

Distinct2179
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6069567
Minimum1.1578947
Maximum2.6078431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.840273image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1.1578947
5-th percentile1.4074613
Q11.5128205
median1.5890411
Q31.6857143
95-th percentile1.8571429
Maximum2.6078431
Range1.4499484
Interquartile range (IQR)0.17289377

Descriptive statistics

Standard deviation0.13834106
Coefficient of variation (CV)0.086088855
Kurtosis1.5719666
Mean1.6069567
Median Absolute Deviation (MAD)0.085377509
Skewness0.75097256
Sum70841.08
Variance0.01913825
MonotonicityNot monotonic
2025-04-19T09:14:00.897940image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5 1921
 
4.4%
1.571428571 1508
 
3.4%
1.625 1138
 
2.6%
1.666666667 1047
 
2.4%
1.512820513 762
 
1.7%
1.6 544
 
1.2%
1.714285714 520
 
1.2%
1.428571429 380
 
0.9%
1.555555556 363
 
0.8%
1.75 342
 
0.8%
Other values (2169) 35559
80.7%
ValueCountFrequency (%)
1.157894737 1
 
< 0.1%
1.162790698 1
 
< 0.1%
1.163934426 1
 
< 0.1%
1.170212766 1
 
< 0.1%
1.172727273 1
 
< 0.1%
1.181818182 1
 
< 0.1%
1.188235294 1
 
< 0.1%
1.195402299 1
 
< 0.1%
1.2 4
< 0.1%
1.204819277 1
 
< 0.1%
ValueCountFrequency (%)
2.607843137 1
< 0.1%
2.565217391 1
< 0.1%
2.537037037 1
< 0.1%
2.5 2
< 0.1%
2.476190476 1
< 0.1%
2.470588235 1
< 0.1%
2.468085106 1
< 0.1%
2.428571429 1
< 0.1%
2.411764706 1
< 0.1%
2.409836066 1
< 0.1%

cholesterol_ratio
Real number (ℝ)

High correlation 

Distinct7720
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6208839
Minimum0.38349515
Maximum52.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:00.956762image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.38349515
5-th percentile2.2068966
Q12.859375
median3.4827586
Q34.2364833
95-th percentile5.4790308
Maximum52.5
Range52.116505
Interquartile range (IQR)1.3771083

Descriptive statistics

Standard deviation1.072312
Coefficient of variation (CV)0.29614647
Kurtosis169.67898
Mean3.6208839
Median Absolute Deviation (MAD)0.67868438
Skewness4.5195863
Sum159623.05
Variance1.149853
MonotonicityNot monotonic
2025-04-19T09:14:01.016107image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 290
 
0.7%
4 273
 
0.6%
3.5 184
 
0.4%
5 128
 
0.3%
3.666666667 116
 
0.3%
3.333333333 111
 
0.3%
4.5 96
 
0.2%
2.5 83
 
0.2%
3.25 71
 
0.2%
2.666666667 70
 
0.2%
Other values (7710) 42662
96.8%
ValueCountFrequency (%)
0.3834951456 1
< 0.1%
0.5598885794 1
< 0.1%
1.12244898 1
< 0.1%
1.213483146 1
< 0.1%
1.265060241 1
< 0.1%
1.293478261 1
< 0.1%
1.295238095 1
< 0.1%
1.3 1
< 0.1%
1.326086957 1
< 0.1%
1.340425532 1
< 0.1%
ValueCountFrequency (%)
52.5 1
< 0.1%
48.75 1
< 0.1%
16.35714286 1
< 0.1%
10.42857143 1
< 0.1%
9.897435897 1
< 0.1%
9.888888889 1
< 0.1%
9.822222222 1
< 0.1%
9.612903226 1
< 0.1%
9.333333333 1
< 0.1%
9.24 1
< 0.1%

ldl_hdl_ratio
Real number (ℝ)

High correlation 

Distinct7048
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1372885
Minimum0.02173913
Maximum51.714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size688.8 KiB
2025-04-19T09:14:01.073518image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.02173913
5-th percentile0.96551724
Q11.5294118
median2.04
Q32.625
95-th percentile3.6120748
Maximum51.714286
Range51.692547
Interquartile range (IQR)1.0955882

Descriptive statistics

Standard deviation0.97630987
Coefficient of variation (CV)0.45679836
Kurtosis416.55529
Mean2.1372885
Median Absolute Deviation (MAD)0.54490566
Skewness10.899397
Sum94220.226
Variance0.95318096
MonotonicityNot monotonic
2025-04-19T09:14:01.131033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 399
 
0.9%
3 205
 
0.5%
2.5 176
 
0.4%
1.5 171
 
0.4%
1 143
 
0.3%
1.666666667 128
 
0.3%
2.333333333 126
 
0.3%
2.666666667 106
 
0.2%
2.25 104
 
0.2%
1.333333333 100
 
0.2%
Other values (7038) 42426
96.2%
ValueCountFrequency (%)
0.02173913043 1
< 0.1%
0.0243902439 1
< 0.1%
0.08974358974 1
< 0.1%
0.1460674157 1
< 0.1%
0.152173913 1
< 0.1%
0.1523809524 1
< 0.1%
0.16 1
< 0.1%
0.1604938272 1
< 0.1%
0.1643835616 1
< 0.1%
0.1686746988 1
< 0.1%
ValueCountFrequency (%)
51.71428571 1
< 0.1%
45.36585366 1
< 0.1%
42 1
< 0.1%
40.25 1
< 0.1%
33.19148936 1
< 0.1%
27.45098039 1
< 0.1%
25.28301887 1
< 0.1%
25 1
< 0.1%
21.53846154 1
< 0.1%
21.28205128 1
< 0.1%

hypertension
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.4 MiB
0
39774 
1
4310 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters44084
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 39774
90.2%
1 4310
 
9.8%

Length

2025-04-19T09:14:01.180868image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-04-19T09:14:01.221815image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
0 39774
90.2%
1 4310
 
9.8%

Most occurring characters

ValueCountFrequency (%)
0 39774
90.2%
1 4310
 
9.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 39774
90.2%
1 4310
 
9.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 39774
90.2%
1 4310
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 39774
90.2%
1 4310
 
9.8%

Interactions

2025-04-19T09:13:56.825153image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:33.092777image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:34.313204image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:35.321338image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:36.601643image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:37.626713image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:38.590084image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:39.596699image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:40.893657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:41.857340image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:42.823946image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:43.845418image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:45.163435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:46.158701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:47.162321image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:48.181936image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:49.160615image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:50.524344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:51.473404image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:52.445911image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:53.402893image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:54.354985image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:55.401732image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:56.866682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:33.137305image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:34.356472image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:35.364351image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:36.649202image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:37.667958image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:38.633924image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:39.640664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:40.935724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:41.899679image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:42.868845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:43.887804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:45.206544image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:46.202611image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:47.206506image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:48.233143image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:49.203503image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:50.565857image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:51.515592image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:52.487628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:53.444210image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:54.399046image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:55.444614image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:56.909691image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:33.180685image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
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2025-04-19T09:13:45.988701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:46.989555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:47.989570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:48.991377image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:50.355628image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:51.308990image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:52.277583image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:53.237522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:54.191510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:55.224021image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:56.660052image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:57.662978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:34.185776image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:35.188786image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:36.192848image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:37.493253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:38.456177image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:39.463973image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:40.765375image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:41.731982image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:42.698542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:43.711931image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:45.034345image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:46.030862image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:47.031829image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:48.031395image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:49.035595image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:50.396336image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:51.348523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:52.318433image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:53.277638image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:54.230988image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:55.267782image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:56.700152image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:57.706528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:34.230478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:35.235095image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:36.511635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:37.541607image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:38.502895image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:39.511455image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:40.810096image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:41.775737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:42.742561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:43.759181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:45.078676image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:46.075559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:47.077307image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:48.079556image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:49.079772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:50.441206image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:51.392587image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:52.363568image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:53.321735image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:54.274422image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:55.314000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:56.744625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:57.747467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:34.272262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:35.278227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:36.554898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:37.584126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:38.546947image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:39.553969image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:40.852003image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:41.817380image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:42.783858image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:43.802569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:45.122692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:46.117871image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:47.119804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:48.129522image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:49.120267image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:50.482834image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:51.433245image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:52.404737image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:53.362097image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:54.315156image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:55.357913image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-04-19T09:13:56.784480image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-04-19T09:14:01.268772image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
agealtastbmibp_ratiocholesterolcholesterol_ratiodental_carieseyesight_lefteyesight_rightfasting_blood_sugargendergtphdlhearing_lefthearing_rightheight_cmhemoglobinhypertensionldlldl_hdl_ratiorelaxationserum_creatininesmokingsystolictartartriglycerideurine_proteinwaist_cmweight_kg
age1.000-0.0740.113-0.0570.0660.0760.0240.124-0.339-0.3360.2110.434-0.0220.0200.2470.251-0.498-0.3170.1350.0650.0240.052-0.1770.1730.1110.0980.0300.009-0.038-0.339
alt-0.0741.0000.7260.406-0.0420.1070.2960.0000.0570.0610.1880.0000.614-0.2660.0280.0280.2660.4160.0000.0900.2250.2030.2330.0000.2000.0000.3510.0160.4550.446
ast0.1130.7261.0000.211-0.0160.1010.1310.000-0.026-0.0250.1060.0140.458-0.0810.0000.0000.0730.2250.0120.0620.0900.1540.1540.0100.1630.0000.1860.0180.2420.190
bmi-0.0570.4060.2111.000-0.0190.0980.3570.0370.0400.0390.2190.2400.360-0.3380.0110.0150.1590.2660.1550.1200.2910.2690.1870.1150.2970.0360.3460.0150.8020.798
bp_ratio0.066-0.042-0.016-0.0191.000-0.061-0.0590.008-0.052-0.050-0.0040.044-0.0750.0210.0610.061-0.056-0.1020.169-0.048-0.051-0.487-0.0450.0600.1680.024-0.060-0.013-0.019-0.050
cholesterol0.0760.1070.1010.098-0.0611.0000.4920.000-0.009-0.0080.0520.0880.1390.1580.0300.027-0.0810.0490.0460.8920.5580.0920.0160.0350.0530.0060.250-0.0130.0680.021
cholesterol_ratio0.0240.2960.1310.357-0.0590.4921.0000.0000.0160.0200.1420.0830.281-0.7490.0000.0000.1480.2680.0290.6280.9470.1440.1910.0810.1180.0250.5770.0000.3840.344
dental_caries0.1240.0000.0000.0370.0080.0000.0001.0000.0230.0240.0180.0860.0160.0130.0160.0200.0830.0690.0080.0000.0100.0190.0000.1040.0210.1720.0200.0050.0340.067
eyesight_left-0.3390.057-0.0260.040-0.052-0.0090.0160.0231.0000.695-0.0600.1630.043-0.0230.0680.0670.2430.1610.038-0.0080.0110.0060.1070.078-0.0360.0140.021-0.0130.0380.177
eyesight_right-0.3360.061-0.0250.039-0.050-0.0080.0200.0240.6951.000-0.0600.1610.045-0.0260.0670.0690.2480.1650.039-0.0070.0140.0070.1060.082-0.0330.0140.023-0.0120.0400.179
fasting_blood_sugar0.2110.1880.1060.219-0.0040.0520.1420.018-0.060-0.0601.0000.1080.270-0.1330.0250.0250.0280.1080.0970.0120.0790.1900.0720.0930.2180.0220.2620.0290.2530.174
gender0.4340.0000.0140.2400.0440.0880.0830.0860.1630.1610.1081.0000.1330.2480.0120.0110.7780.7350.0430.0290.0110.1640.1130.5080.1850.0560.2300.0180.4340.613
gtp-0.0220.6140.4580.360-0.0750.1390.2810.0160.0430.0450.2700.1331.000-0.2220.0000.0000.2960.4450.0700.0640.1780.2720.2860.1580.2550.0200.4540.0220.4620.433
hdl0.020-0.266-0.081-0.3380.0210.158-0.7490.013-0.023-0.026-0.1330.248-0.2221.0000.0070.009-0.229-0.2720.032-0.058-0.640-0.099-0.2100.143-0.0990.030-0.470-0.013-0.393-0.378
hearing_left0.2470.0280.0000.0110.0610.0300.0000.0160.0680.0670.0250.0120.0000.0071.0000.4810.0850.0390.0320.0000.0000.0000.0000.0220.0500.0310.0000.0070.0260.052
hearing_right0.2510.0280.0000.0150.0610.0270.0000.0200.0670.0690.0250.0110.0000.0090.4811.0000.0810.0420.0310.0000.0000.0000.0120.0160.0480.0250.0000.0000.0180.051
height_cm-0.4980.2660.0730.159-0.056-0.0810.1480.0830.2430.2480.0280.7780.296-0.2290.0850.0811.0000.5830.021-0.0540.1040.1190.4760.4160.0960.0540.1670.0090.3880.698
hemoglobin-0.3170.4160.2250.266-0.1020.0490.2680.0690.1610.1650.1080.7350.445-0.2720.0390.0420.5831.0000.0780.0570.2100.2300.4900.4050.1880.0580.2920.0240.3960.538
hypertension0.1350.0000.0120.1550.1690.0460.0290.0080.0380.0390.0970.0430.0700.0320.0320.0310.0210.0781.0000.0050.0000.7180.0200.0080.7980.0060.1010.0450.1580.115
ldl0.0650.0900.0620.120-0.0480.8920.6280.000-0.008-0.0070.0120.0290.064-0.0580.0000.000-0.0540.0570.0051.0000.7730.0550.0450.0120.0200.0060.093-0.0120.0940.054
ldl_hdl_ratio0.0240.2250.0900.291-0.0510.5580.9470.0100.0110.0140.0790.0110.178-0.6400.0000.0000.1040.2100.0000.7731.0000.0980.1620.0110.0700.0000.351-0.0020.3040.270
relaxation0.0520.2030.1540.269-0.4870.0920.1440.0190.0060.0070.1900.1640.272-0.0990.0000.0000.1190.2300.7180.0550.0981.0000.0940.0930.7390.0210.2240.0210.2820.262
serum_creatinine-0.1770.2330.1540.187-0.0450.0160.1910.0000.1070.1060.0720.1130.286-0.2100.0000.0120.4760.4900.0200.0450.1620.0941.0000.0240.0770.0060.1570.0110.2830.419
smoking0.1730.0000.0100.1150.0600.0350.0810.1040.0780.0820.0930.5080.1580.1430.0220.0160.4160.4050.0080.0120.0110.0930.0241.0000.0880.1010.2360.0080.2250.311
systolic0.1110.2000.1630.2970.1680.0530.1180.021-0.036-0.0330.2180.1850.255-0.0990.0500.0480.0960.1880.7980.0200.0700.7390.0770.0881.0000.0050.2120.0160.3120.265
tartar0.0980.0000.0000.0360.0240.0060.0250.1720.0140.0140.0220.0560.0200.0300.0310.0250.0540.0580.0060.0060.0000.0210.0060.1010.0051.0000.0370.0110.0450.054
triglyceride0.0300.3510.1860.346-0.0600.2500.5770.0200.0210.0230.2620.2300.454-0.4700.0000.0000.1670.2920.1010.0930.3510.2240.1570.2360.2120.0371.0000.0060.3960.345
urine_protein0.0090.0160.0180.015-0.013-0.0130.0000.005-0.013-0.0120.0290.0180.022-0.0130.0070.0000.0090.0240.045-0.012-0.0020.0210.0110.0080.0160.0110.0061.0000.0240.017
waist_cm-0.0380.4550.2420.802-0.0190.0680.3840.0340.0380.0400.2530.4340.462-0.3930.0260.0180.3880.3960.1580.0940.3040.2820.2830.2250.3120.0450.3960.0241.0000.806
weight_kg-0.3390.4460.1900.798-0.0500.0210.3440.0670.1770.1790.1740.6130.433-0.3780.0520.0510.6980.5380.1150.0540.2700.2620.4190.3110.2650.0540.3450.0170.8061.000

Missing values

2025-04-19T09:13:57.821664image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-19T09:13:58.010441image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

genderageheight_cmweight_kgwaist_cmeyesight_lefteyesight_righthearing_lefthearing_rightsystolicrelaxationfasting_blood_sugarcholesteroltriglyceridehdlldlhemoglobinurine_proteinserum_creatinineastaltgtpdental_cariestartarsmokingbmibp_ratiocholesterol_ratioldl_hdl_ratiohypertension
00401556081.31.21.01.01.0114.073.094.0215.082.073.0126.012.91.00.718.019.027.001024.9739851.5616442.9452051.7260270
10401606081.00.80.61.01.0119.070.0130.0192.0115.042.0127.012.71.00.622.019.018.001023.4375001.7000004.5714293.0238100
21551706080.00.80.81.01.0138.086.089.0242.0182.055.0151.015.81.01.021.016.022.000120.7612461.6046514.4000002.7454550
31401657088.01.51.51.01.0100.060.096.0322.0254.045.0226.014.71.01.019.026.018.001025.7116621.6666677.1555565.0222220
40401556086.01.01.01.01.0120.074.080.0184.074.062.0107.012.51.00.616.014.022.000024.9739851.6216222.9677421.7258060
51301807585.01.21.21.01.0128.076.095.0217.0199.048.0129.016.21.01.218.027.033.001023.1481481.6842114.5208332.6875000
61401606085.51.01.01.01.0116.082.094.0226.068.055.0157.017.01.00.721.027.039.011123.4375001.4146344.1090912.8545450
71451659096.01.21.01.01.0153.096.0158.0222.0269.034.0134.015.01.01.338.071.0111.001033.0578511.5937506.5294123.9411761
80501506085.00.70.81.01.0115.074.086.0210.066.048.0149.013.71.00.831.031.014.000026.6666671.5540544.3750003.1041670
91451757589.01.01.01.01.0113.064.094.0198.0147.043.0126.016.01.00.826.024.063.000024.4897961.7656254.6046512.9302330
genderageheight_cmweight_kgwaist_cmeyesight_lefteyesight_righthearing_lefthearing_rightsystolicrelaxationfasting_blood_sugarcholesteroltriglyceridehdlldlhemoglobinurine_proteinserum_creatinineastaltgtpdental_cariestartarsmokingbmibp_ratiocholesterol_ratioldl_hdl_ratiohypertension
445431201706576.00.90.71.01.0110.070.079.0184.063.057.0114.015.81.01.015.013.014.001022.4913491.5714293.2280702.0000000
445440451654558.00.91.21.01.0133.075.0110.0190.086.063.0110.013.01.00.820.016.010.001016.5289261.7733333.0158731.7460320
445451451859599.01.01.01.01.0126.086.0105.0207.0131.050.0130.014.01.01.031.048.032.001027.7574871.4651164.1400002.6000000
445461601605577.00.70.21.01.0118.068.0107.0192.0112.064.0106.015.51.01.235.021.017.000121.4843751.7352943.0000001.6562500
445471601657087.00.61.01.01.0124.078.0107.0193.0109.048.0123.015.51.01.120.017.032.010125.7116621.5897444.0208332.5625000
445481501706588.01.21.21.01.0148.094.0102.0182.0168.040.0108.015.21.01.045.049.051.001022.4913491.5744684.5500002.7000001
445491351757084.00.60.71.01.0105.074.088.0187.060.057.0118.015.21.00.920.014.016.001022.8571431.4189193.2807022.0701750
445501351757070.91.51.51.01.0116.070.095.0145.058.041.092.013.41.01.018.019.010.001022.8571431.6571433.5365852.2439020
4455115016590106.80.80.51.01.0122.075.091.0179.0139.047.0104.014.51.00.930.049.040.001133.0578511.6266673.8085112.2127660
445521251758093.21.21.51.01.0124.076.078.0197.051.049.0138.011.41.01.023.011.020.001026.1224491.6315794.0204082.8163270